Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening.

Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

Automated retinal nerve fiber layer defect detection using fundus imaging in glaucoma.

Retinal nerve fiber layer defect (RNFLD) provides an early objective evidence of structural changes in glaucoma. RNFLD detection is currently carried out using imaging modalities like OCT and GDx which are expensive for routine practice. In this regard, we propose a novel automatic method for RNFLD detection and angular width quantification using cost effective redfree fundus images to be practically useful for computer-assisted glaucoma risk assessment. After blood vessel inpainting and CLAHE based contrast enhancement, the initial boundary pixels are identified by local minima analysis of the 1-D intensity profiles on concentric circles. The true boundary pixels are classified using random forest trained by newly proposed cumulative zero count local binary pattern (CZC-LBP) and directional differential energy (DDE) along with Shannon, Tsallis entropy and intensity features. Finally, the RNFLD angular width is obtained by random sample consensus (RANSAC) line fitting on the detected set of boundary pixels. The proposed method is found to achieve high RNFLD detection performance on a newly created dataset with sensitivity (SN) of 0.7821 at 0.2727 false positives per image (FPI) and the area under curve (AUC) value is obtained as 0.8733.

Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma.

Glaucoma is a progressive optic neuropathy characterized by peripheral visual field loss, which is caused by degeneration of retinal nerve fibers. The peripheral vision loss due to glaucoma is asymptomatic. If not detected and treated at an early stage, it leads to complete blindness, which is irreversible in nature. The retinal nerve fiber layer defect (RNFLD) provides an earliest objective evidence of glaucoma. In this regard, we explore cost-effective redfree fundus imaging for RNFLD detection to be practically useful for computer-assisted early glaucoma risk assessment. RNFLD appears as a wedge shaped arcuate structure radiating from the optic disc. The very low contrast between RNFLD and background makes its visual detection quite challenging even by medical experts. In our study, we formulate a deep convolutional neural network (CNN) based patch classification strategy for RNFLD boundary localization. A large number of RNFLD and background image patches train the deep CNN model, which extracts sufficient discriminative information from the patches and results in accurate RNFLD boundary pixel classification. The proposed approach is found to achieve enhanced RNFLD detection performance with sensitivity of 0.8205 and false positive per image of 0.2000 on a newly created early glaucomatic fundus image database.

Hot melt granulation: a facile approach for monolithic osmotic release tablets.

The aim of this work was to develop and evaluate an extended release matrix tablet of glipizide (GP), an oral hypoglycemic agent. Matrices of GP were prepared using microcrystalline cellulose Avicel(™) PH 112, sodium chloride (SC) and polyethylene glycol 6000 (PEG). The content of Kollidon SR (KR), hydroxypropyl methylcellulose K4M premium CR grade (HM) and polyethylene oxide WSR 303 (PO) and/or magnesium hydroxide (MH) was varied in different formulations. All the formulations were processed by hot melt granulation technique. GP release was observed to be influenced by the amount of SC and MH present in the core formulation. The matrix tablets were coated with a solution containing combination of cellulose acetate 398.10 (CA) and PEG. The release of GP was observed to be inversely proportional to the weight of the coating membrane. Matrices containing PO in combination with SC and MH (14.28:8.56) showed significantly higher degree of hydration and swelling that was evident in the surface texture as visualized by scanning electron microscopy (SEM). Results of SEM studies confirmed the presence of pores in the semi-permeable coating membrane from where the GP release would have occurred. The release of GP from this formulation was similar to that of the marketed extended release tablet as judged from similarity factor (f2) analysis, which yielded a value of 74.7. The optimized formulation was found to be stable when tested according to long term and accelerated storage conditions of ICH guidelines upto 3 months.

Formulation and optimization of osmotically controlled release tablets of glipizide: hot melt granulation.

BACKGROUND: The use of water-soluble additives in osmotic release tablets often renders the wet granulation method unsuitable. Hence, it was proposed to investigate the feasibility of preparing granules comprising of osmogen (sodium chloride), alkalizer (sodium carbonate), polyvinyl pyrroidone (Kollidon K 30) and carboxymethyl cellulose sodium (Cekol 30000) by a hot melt technique for obtaining sustained release of glipizide from tablets. MATERIALS AND METHODS: Dry powder mixture wetted with Tween 80 was sequentially exposed to 45-110 degrees C for obtaining granules. The effect of varying the quantities of osmogen, Cekol 30000 in granules and pore former in the film coating on in vitro release of glipizide was investigated. RESULTS: Glipizide release increased with an increase in the amount of hydrophilic polymer, osmogen and alkalizer and decreased with increase in the thickness of the coating membrane. Zero-order release independent of stirring rate and media pH through 16 h was observed from tablets of the optimized formulation (glipizide osmotic tablet [GOT] 12). The f2 value of 70.2 indicated similarity in release profiles from this formulation and the marketed extended-release tablet. Accelerated tests (ICH guidelines) revealed stability of GOT 12 tablets.